The objective of this project is to use historical loan application data to predict whether or not an applicant will be able to repay a loan.
- The distribution of data in the training set is imbalanced.
- Lets understand the number of empty cells in the dataframe i.e. the missing data.
- The different types of features in the dataframe
| Feature Type | Number of featues |
|---|---|
| float64 | 65 |
| int64 | 41 |
| object | 16 |
- We need to handle the 16 categorical variables in the dataset. Lets check the number of uniques values in each of these 16 categorical featues.
| Features | Distinct Values |
|---|---|
| NAME_CONTRACT_TYPE | 2 |
| CODE_GENDER | 3 |
| FLAG_OWN_CAR | 2 |
| FLAG_OWN_REALTY | 2 |
| NAME_TYPE_SUITE | 7 |
| NAME_INCOME_TYPE | 8 |
| NAME_EDUCATION_TYPE | 5 |
| NAME_FAMILY_STATUS | 6 |
| NAME_HOUSING_TYPE | 6 |
| OCCUPATION_TYPE | 18 |
| WEEKDAY_APPR_PROCESS_START | 7 |
| ORGANIZATION_TYPE | 58 |
| FONDKAPREMONT_MODE | 4 |
| HOUSETYPE_MODE | 3 |
| WALLSMATERIAL_MODE | 7 |
| EMERGENCYSTATE_MODE | 2 |